{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "logical xor\n",
      "epoch 0 sample 0\n",
      "ω_1和b1 的变化 ： 1.5 1.5 -1 0 0 0 0\n",
      "ω_2和b2 的变化 ： -2 -2 3 1 0 0 0\n",
      "ω和b 的变化 ：    2 2 -3 0 0 0 0\n",
      "epoch 0 sample 1\n",
      "ω_1和b1 的变化 ： 1.5 1.5 -1 1 0 0 0\n",
      "ω_2和b2 的变化 ： -2 -2 3 1 0 0 0\n",
      "ω和b 的变化 ：    2 2 -3 1 0 0 0\n",
      "epoch 0 sample 2\n",
      "ω_1和b1 的变化 ： 1.5 1.5 -1 1 0 0 0\n",
      "ω_2和b2 的变化 ： -2 -2 3 1 0 0 0\n",
      "ω和b 的变化 ：    2 2 -3 1 0 0 0\n",
      "epoch 0 sample 3\n",
      "ω_1和b1 的变化 ： 1.5 1.5 -1 1 0 0 0\n",
      "ω_2和b2 的变化 ： -2 -2 3 0 0 0 0\n",
      "ω和b 的变化 ：    2 2 -3 0 0 0 0\n",
      "=======================分割线==========================\n",
      "epoch 1 sample 0\n",
      "ω_1和b1 的变化 ： 1.5 1.5 -1 0 0 0 0\n",
      "ω_2和b2 的变化 ： -2 -2 3 1 0 0 0\n",
      "ω和b 的变化 ：    2 2 -3 0 0 0 0\n",
      "epoch 1 sample 1\n",
      "ω_1和b1 的变化 ： 1.5 1.5 -1 1 0 0 0\n",
      "ω_2和b2 的变化 ： -2 -2 3 1 0 0 0\n",
      "ω和b 的变化 ：    2 2 -3 1 0 0 0\n",
      "epoch 1 sample 2\n",
      "ω_1和b1 的变化 ： 1.5 1.5 -1 1 0 0 0\n",
      "ω_2和b2 的变化 ： -2 -2 3 1 0 0 0\n",
      "ω和b 的变化 ：    2 2 -3 1 0 0 0\n",
      "epoch 1 sample 3\n",
      "ω_1和b1 的变化 ： 1.5 1.5 -1 1 0 0 0\n",
      "ω_2和b2 的变化 ： -2 -2 3 0 0 0 0\n",
      "ω和b 的变化 ：    2 2 -3 0 0 0 0\n",
      "=======================分割线==========================\n",
      "epoch 2 sample 0\n",
      "ω_1和b1 的变化 ： 1.5 1.5 -1 0 0 0 0\n",
      "ω_2和b2 的变化 ： -2 -2 3 1 0 0 0\n",
      "ω和b 的变化 ：    2 2 -3 0 0 0 0\n",
      "epoch 2 sample 1\n",
      "ω_1和b1 的变化 ： 1.5 1.5 -1 1 0 0 0\n",
      "ω_2和b2 的变化 ： -2 -2 3 1 0 0 0\n",
      "ω和b 的变化 ：    2 2 -3 1 0 0 0\n",
      "epoch 2 sample 2\n",
      "ω_1和b1 的变化 ： 1.5 1.5 -1 1 0 0 0\n",
      "ω_2和b2 的变化 ： -2 -2 3 1 0 0 0\n",
      "ω和b 的变化 ：    2 2 -3 1 0 0 0\n",
      "epoch 2 sample 3\n",
      "ω_1和b1 的变化 ： 1.5 1.5 -1 1 0 0 0\n",
      "ω_2和b2 的变化 ： -2 -2 3 0 0 0 0\n",
      "ω和b 的变化 ：    2 2 -3 0 0 0 0\n",
      "=======================分割线==========================\n",
      "epoch 3 sample 0\n",
      "ω_1和b1 的变化 ： 1.5 1.5 -1 0 0 0 0\n",
      "ω_2和b2 的变化 ： -2 -2 3 1 0 0 0\n",
      "ω和b 的变化 ：    2 2 -3 0 0 0 0\n",
      "epoch 3 sample 1\n",
      "ω_1和b1 的变化 ： 1.5 1.5 -1 1 0 0 0\n",
      "ω_2和b2 的变化 ： -2 -2 3 1 0 0 0\n",
      "ω和b 的变化 ：    2 2 -3 1 0 0 0\n",
      "epoch 3 sample 2\n",
      "ω_1和b1 的变化 ： 1.5 1.5 -1 1 0 0 0\n",
      "ω_2和b2 的变化 ： -2 -2 3 1 0 0 0\n",
      "ω和b 的变化 ：    2 2 -3 1 0 0 0\n",
      "epoch 3 sample 3\n",
      "ω_1和b1 的变化 ： 1.5 1.5 -1 1 0 0 0\n",
      "ω_2和b2 的变化 ： -2 -2 3 0 0 0 0\n",
      "ω和b 的变化 ：    2 2 -3 0 0 0 0\n",
      "=======================分割线==========================\n",
      "epoch 4 sample 0\n",
      "ω_1和b1 的变化 ： 1.5 1.5 -1 0 0 0 0\n",
      "ω_2和b2 的变化 ： -2 -2 3 1 0 0 0\n",
      "ω和b 的变化 ：    2 2 -3 0 0 0 0\n",
      "epoch 4 sample 1\n",
      "ω_1和b1 的变化 ： 1.5 1.5 -1 1 0 0 0\n",
      "ω_2和b2 的变化 ： -2 -2 3 1 0 0 0\n",
      "ω和b 的变化 ：    2 2 -3 1 0 0 0\n",
      "epoch 4 sample 2\n",
      "ω_1和b1 的变化 ： 1.5 1.5 -1 1 0 0 0\n",
      "ω_2和b2 的变化 ： -2 -2 3 1 0 0 0\n",
      "ω和b 的变化 ：    2 2 -3 1 0 0 0\n",
      "epoch 4 sample 3\n",
      "ω_1和b1 的变化 ： 1.5 1.5 -1 1 0 0 0\n",
      "ω_2和b2 的变化 ： -2 -2 3 0 0 0 0\n",
      "ω和b 的变化 ：    2 2 -3 0 0 0 0\n",
      "=======================分割线==========================\n",
      "epoch 5 sample 0\n",
      "ω_1和b1 的变化 ： 1.5 1.5 -1 0 0 0 0\n",
      "ω_2和b2 的变化 ： -2 -2 3 1 0 0 0\n",
      "ω和b 的变化 ：    2 2 -3 0 0 0 0\n",
      "epoch 5 sample 1\n",
      "ω_1和b1 的变化 ： 1.5 1.5 -1 1 0 0 0\n",
      "ω_2和b2 的变化 ： -2 -2 3 1 0 0 0\n",
      "ω和b 的变化 ：    2 2 -3 1 0 0 0\n",
      "epoch 5 sample 2\n",
      "ω_1和b1 的变化 ： 1.5 1.5 -1 1 0 0 0\n",
      "ω_2和b2 的变化 ： -2 -2 3 1 0 0 0\n",
      "ω和b 的变化 ：    2 2 -3 1 0 0 0\n",
      "epoch 5 sample 3\n",
      "ω_1和b1 的变化 ： 1.5 1.5 -1 1 0 0 0\n",
      "ω_2和b2 的变化 ： -2 -2 3 0 0 0 0\n",
      "ω和b 的变化 ：    2 2 -3 0 0 0 0\n",
      "=======================分割线==========================\n",
      "epoch 6 sample 0\n",
      "ω_1和b1 的变化 ： 1.5 1.5 -1 0 0 0 0\n",
      "ω_2和b2 的变化 ： -2 -2 3 1 0 0 0\n",
      "ω和b 的变化 ：    2 2 -3 0 0 0 0\n",
      "epoch 6 sample 1\n",
      "ω_1和b1 的变化 ： 1.5 1.5 -1 1 0 0 0\n",
      "ω_2和b2 的变化 ： -2 -2 3 1 0 0 0\n",
      "ω和b 的变化 ：    2 2 -3 1 0 0 0\n",
      "epoch 6 sample 2\n",
      "ω_1和b1 的变化 ： 1.5 1.5 -1 1 0 0 0\n",
      "ω_2和b2 的变化 ： -2 -2 3 1 0 0 0\n",
      "ω和b 的变化 ：    2 2 -3 1 0 0 0\n",
      "epoch 6 sample 3\n",
      "ω_1和b1 的变化 ： 1.5 1.5 -1 1 0 0 0\n",
      "ω_2和b2 的变化 ： -2 -2 3 0 0 0 0\n",
      "ω和b 的变化 ：    2 2 -3 0 0 0 0\n",
      "=======================分割线==========================\n",
      "epoch 7 sample 0\n",
      "ω_1和b1 的变化 ： 1.5 1.5 -1 0 0 0 0\n",
      "ω_2和b2 的变化 ： -2 -2 3 1 0 0 0\n",
      "ω和b 的变化 ：    2 2 -3 0 0 0 0\n",
      "epoch 7 sample 1\n",
      "ω_1和b1 的变化 ： 1.5 1.5 -1 1 0 0 0\n",
      "ω_2和b2 的变化 ： -2 -2 3 1 0 0 0\n",
      "ω和b 的变化 ：    2 2 -3 1 0 0 0\n",
      "epoch 7 sample 2\n",
      "ω_1和b1 的变化 ： 1.5 1.5 -1 1 0 0 0\n",
      "ω_2和b2 的变化 ： -2 -2 3 1 0 0 0\n",
      "ω和b 的变化 ：    2 2 -3 1 0 0 0\n",
      "epoch 7 sample 3\n",
      "ω_1和b1 的变化 ： 1.5 1.5 -1 1 0 0 0\n",
      "ω_2和b2 的变化 ： -2 -2 3 0 0 0 0\n",
      "ω和b 的变化 ：    2 2 -3 0 0 0 0\n",
      "=======================分割线==========================\n",
      "epoch 8 sample 0\n",
      "ω_1和b1 的变化 ： 1.5 1.5 -1 0 0 0 0\n",
      "ω_2和b2 的变化 ： -2 -2 3 1 0 0 0\n",
      "ω和b 的变化 ：    2 2 -3 0 0 0 0\n",
      "epoch 8 sample 1\n",
      "ω_1和b1 的变化 ： 1.5 1.5 -1 1 0 0 0\n",
      "ω_2和b2 的变化 ： -2 -2 3 1 0 0 0\n",
      "ω和b 的变化 ：    2 2 -3 1 0 0 0\n",
      "epoch 8 sample 2\n",
      "ω_1和b1 的变化 ： 1.5 1.5 -1 1 0 0 0\n",
      "ω_2和b2 的变化 ： -2 -2 3 1 0 0 0\n",
      "ω和b 的变化 ：    2 2 -3 1 0 0 0\n",
      "epoch 8 sample 3\n",
      "ω_1和b1 的变化 ： 1.5 1.5 -1 1 0 0 0\n",
      "ω_2和b2 的变化 ： -2 -2 3 0 0 0 0\n",
      "ω和b 的变化 ：    2 2 -3 0 0 0 0\n",
      "=======================分割线==========================\n",
      "epoch 9 sample 0\n",
      "ω_1和b1 的变化 ： 1.5 1.5 -1 0 0 0 0\n",
      "ω_2和b2 的变化 ： -2 -2 3 1 0 0 0\n",
      "ω和b 的变化 ：    2 2 -3 0 0 0 0\n",
      "epoch 9 sample 1\n",
      "ω_1和b1 的变化 ： 1.5 1.5 -1 1 0 0 0\n",
      "ω_2和b2 的变化 ： -2 -2 3 1 0 0 0\n",
      "ω和b 的变化 ：    2 2 -3 1 0 0 0\n",
      "epoch 9 sample 2\n",
      "ω_1和b1 的变化 ： 1.5 1.5 -1 1 0 0 0\n",
      "ω_2和b2 的变化 ： -2 -2 3 1 0 0 0\n",
      "ω和b 的变化 ：    2 2 -3 1 0 0 0\n",
      "epoch 9 sample 3\n",
      "ω_1和b1 的变化 ： 1.5 1.5 -1 1 0 0 0\n",
      "ω_2和b2 的变化 ： -2 -2 3 0 0 0 0\n",
      "ω和b 的变化 ：    2 2 -3 0 0 0 0\n",
      "=======================分割线==========================\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# -*- coding:utf-8 -*-\n",
    "# Author: Winder\n",
    "\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 图形出现在Notebook里而不是新窗口\n",
    "%matplotlib inline\n",
    "\n",
    "#逻辑异或数据\n",
    "samples_xor = [\n",
    "    [0, 0, 0],\n",
    "    [1, 0, 1],\n",
    "    [0, 1, 1],\n",
    "    [1, 1, 0],\n",
    "]\n",
    "\n",
    "def perceptron(samples):\n",
    "    #不断的选取w参数,理由是次啊用梯度下降法只能选到局部最小值,而且阶跃函数有断点,所以能不能收敛,初值起很大作用;\n",
    "    #进过试参发现,这里的w是向量,必须两个系数一致才有可能收敛;\n",
    "    w = np.array([2, 2])\n",
    "    w_1 = np.array([1.5, 1.5])\n",
    "    w_2 = np.array([-2, -2])\n",
    "    b = -3;\n",
    "    b1 = -1;\n",
    "    b2 = 3;\n",
    "    a = 1;\n",
    "\n",
    "    for i in range(10):\n",
    "        for j in range(4):\n",
    "            x = np.array(samples[j][:2])\n",
    "            y_1 = 1 if np.dot(w_1, x) + b1 > 0 else 0\n",
    "            y_2 = 1 if np.dot(w_2, x) + b2 > 0 else 0\n",
    "            xx = np.array([y_1, y_2]);\n",
    "            y_3 = 1 if np.dot(w, xx) + b > 0 else 0\n",
    "            d = np.array(samples[j][2]);\n",
    "\n",
    "            #这里的δ均为已经去掉负号,后面再进行迭代直接是相加\n",
    "            delta_w = a * (d - y_3) * xx ;\n",
    "            delta_b = a * (d - y_3);\n",
    "            #你会发现这里采用梯度下降法进行计算的时候再求导的式子里用的是非激活的预估值,而且还能收敛;\n",
    "            #那就证明这是切实可行的,其问题在于即使没用激活函数,但是每一个反向传播过程中,在计算损失数值的那部分;\n",
    "            #都进行了激活,而我们第二层的输入值也是经过激活的,因此综合这些因素能使函数得以收敛！\n",
    "            delta_w_1 = a * (d - y_3) * x * w[0];\n",
    "            delta_b1 = a * (d - y_3) * w[0];\n",
    "\n",
    "            delta_w_2 = a * (d - y_3) * x * w[1];\n",
    "            delta_b2 = a * (d - y_3) * w[1];\n",
    "\n",
    "            print('epoch {} sample {}'.format( i ,j));\n",
    "            print('ω_1和b1 的变化 ：',w_1[0] ,w_1[1] ,b1 ,y_1 ,delta_w_1[0],delta_w_1[1],delta_b1);\n",
    "            print('ω_2和b2 的变化 ：', w_2[0], w_2[1], b2, y_2, delta_w_2[0], delta_w_2[1], delta_b2);\n",
    "            print('ω和b 的变化 ：   ', w[0], w[1], b, y_3, delta_w[0], delta_w[1], delta_b);\n",
    "\n",
    "            w = w + delta_w;\n",
    "            b = b + delta_b;\n",
    "            w_1 = w_1 + delta_w_1;\n",
    "            b1 = b1 + delta_b1;\n",
    "            w_2 = w_2 + delta_w_2;\n",
    "            b2 = b2 + delta_b2;\n",
    "        print('=======================分割线==========================');\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def line_1(x,y):\n",
    "    y_1 = (y + 1)/2 - x;\n",
    "    return y_1;\n",
    "\n",
    "def line_2(x,y):\n",
    "    y_2 = (y - 3)/(-2) - x;\n",
    "    return y_2;\n",
    "\n",
    "def line_3(x,y):\n",
    "    y_2 = (y + 3)/2 - x;\n",
    "    return y_2;\n",
    "\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    print('logical xor')\n",
    "    perceptron(samples_xor)\n",
    "    plt.figure(1);\n",
    "    x = np.linspace(-1, 2 , 20);\n",
    "    y1 ,y2 = line_1(x,y=0) , line_2(x,y=0);\n",
    "    #plt.subplot(121);\n",
    "    plt.plot(x, y1,label='line_1');\n",
    "    #plt.subplot(122);\n",
    "    plt.plot(x, y2,label='line_2');\n",
    "    plt.plot([0,1],[0,1],'ro',label = '0 point');\n",
    "    plt.plot([1,0],[0,1],'bo',label = '1 point');\n",
    "    plt.title('line(y=0)/L = 1');\n",
    "    plt.xlabel('x1');\n",
    "    plt.ylabel('x2');\n",
    "    plt.legend();\n",
    "    plt.show();\n",
    "    plt.figure(2);\n",
    "    x = np.linspace(-1, 2, 20);\n",
    "    y1, y2 = line_1(x, y=1), line_2(x, y=1);\n",
    "    plt.plot(x, y1, label='line_1');\n",
    "    plt.plot(x, y2, label='line_2');\n",
    "    plt.plot([0, 1], [0, 1], 'ro', label='0 point');\n",
    "    plt.plot([1, 0], [0, 1], 'bo', label='1 point');\n",
    "    #L=1表示第一层的预估值y\n",
    "    plt.title('line(y=1)/L = 1');\n",
    "    plt.xlabel('x1');\n",
    "    plt.ylabel('x2');\n",
    "    plt.legend();\n",
    "    plt.show();\n",
    "    plt.figure(3);\n",
    "    x = np.linspace(-1, 2, 20);\n",
    "    y1, y2 = line_3(x, y=-1), line_3(x, y=0);\n",
    "    y3 = line_3(x,y=1);\n",
    "    plt.plot(x, y1, label='line_3 (-1)');\n",
    "    plt.plot(x, y2, label='line_3 (0)');\n",
    "    plt.plot(x, y3, label='line_3 (1)');\n",
    "    plt.plot([0, 1], [0, 1], 'ro', label='0 point');\n",
    "    plt.plot([1, 0], [0, 1], 'bo', label='1 point');\n",
    "    #L=2表示第二层的预估值Y\n",
    "    plt.title('line(Δy)/L = 2');\n",
    "    #这里的y1和y2是line_1与line_2函数的预估值（激活阶跃函数）\n",
    "    plt.xlabel('y1');\n",
    "    plt.ylabel('y2');\n",
    "    plt.legend();\n",
    "    plt.show();\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
